Real aperture array radar

The evolution of radar proceeded in a number of steps, of which each had a fundamental effect on the performance. In the beginning, the radar was built using all analogue technology with an analogue display. Information extraction was performed at that time by adjusting the dynamic range of the cathode-ray display. A big improvement was obtained when the output data after analogue filtering and signal processing were sampled and digitized. This allowed some post processing of these data. A number of detection techniques with different algorithms and adaptive thresholds could be implemented. The algorithms could be changed by software if necessary. Digital displays provided a clear extraction of the relevant information. With the increasing availability of fast analogue-to-digital converters and computers, the point of digitization could then be shifted more and more towards the antenna such that programmable filters and signal processing algorithms could be applied. This allowed to apply all the flexibility of digital signal processing in the time domain.

The primary objective of this chapter is to introduce the basic array signal processing methods. If such methods are to be implemented in a radar one has to take into account the particular interrelationships of the different radar processing blocks and one has to adapt the related processing. Therefore, another objective of this chapter is to analyse these problems. The antenna array has to be designed to fulfil all requirements of the radar which is typically a multitasking system. In fact, the requirements can often only be fulfilled in a compromise. These problems are pointed out. The principal array processing methods of deterministic pattern shaping and adaptive beamforming are presented as complementary techniques to be considered together. It depends on the application whether more emphasis is put on low sidelobes or on adaptive nulling. Angular super-resolution methods are presented as a special mode for critical tasks. It is shown that combining detection and monopulse estimation with adapted beamforming results in mutual influence that has to be accounted for. Most important are the implications on tracking algorithms. From these a number of rules for the radar management are derived. Results and conclusions presented in this chapter are not definite design rules but should be considered as exemplary to provide awareness where problems may arise.

Direct data domain space-time adaptive processing (D3-STAP) is an interesting approach for multi-channel radar moving target indication. The main difference with respect to conventional stochastic STAP resides in the capability to cancel the interference (both jammers and clutter) using only the information contained in the single range gate under test. Therefore, being implicitly a single snapshot interference cancellation technique, D3-STAP shows several advantages compared to stochastic STAP in fast varying interference scenarios, where the availability of secondary data for interference statistics estimation is limited. In this chapter, a novel approach for D3-STAP is described. This amelioration overcomes the main limitation of D3-STAP in its classical derivation. In fact, target detection performance of D3-STAP is severely deteriorated in case of inaccurate knowledge of target parameters, namely direction of arrival (DOA) and Doppler frequency. To overcome this problem, a robust D3-STAP (viz. RD3-STAP) implementation is shown which takes into account a possible mismatch between nominal and actual target parameters. The approach reformulates the D3-STAP problem in the context of convex optimization, and it can be applied to the different implementations of D3-STAP, namely forward, backward and forward-backward methods. In addition to that, an implementation of RD3-STAP with dimension reducing transformations is shown which limits the number of degrees of freedom. The effectiveness of the proposed approach is shown both in simulated scenarios and by direct application to real data taken from the experimental multi-channel radar system Phased-Array Multi-functional Imaging Radar (PAMIR) developed at Fraunhofer-Institute for high frequency physics and radar techniques; in German: Fraunhofer-Institut fur Hochfrequenzphysik und Radartechnik (FHR). Finally, possible applications of RD3-STAP to multi-channel synthetic aperture radar and to target DOA estimation cases are presented.

The operation of an electronically steered array (ESA) antenna is highly flexible in that a range of control parameters can be reconfigured nearly instantaneously. Consequently, an ESA is capable of executing numerous tasks supporting multiple functions, multiplexed in time and angle. However, this flexibility creates a challenging operation and resources management problem, in that a new radar dwell complete with beam direction and transmit waveform must be chosen within the time taken to execute the previous radar dwell. As controlling the operation of an ESA is beyond the capability of a human operator, automated management techniques are required, which are emerging as key performance factors for the next generation of multifunction radar systems. This chapter gives an overview of automated techniques for managing the operation and resources of an ESA.

Imaging radar

It took almost exactly 100 years from Maxwell's first conception of the theory of electro-magnetic waves and the subsequent experimental proof by Hertz in the nineteenth century until the first patent on Doppler Beam Sharpening (DBS) was issued to Wiley in 1965. DBS was the original attempt to improve the Doppler resolution of moving pulsed radar by exploitation of a synthetically spanned aperture compared to the physical antenna with its very limited resolution. Although somewhat rudimentary because of the neglected quadratic phase history and thereby still a range-dependent azimuth resolution, it is considered the cornerstone of Synthetic Aperture or imaging Radar (SAR). The fundamental idea here is that through the motion of the radar-carrying platform a long synthetic aperture is spanned, along which the radar raw data are focussed, to achieve a high resolution in the flight direction. Besides the essential platform motion, the other vital attribute of SAR is that the recorded data by no means constitute the desired image but instead must first be processed using suitable focussing algorithms, which among others require a precise knowledge of the platform trajectory. At the onset of SAR, owing to the absence of digital signal processors, the focussing could only be achieved using systems of optical lenses whereby the SAR raw data were recorded on film. This technology was not flexible and only permitted rather moderate results that typically suffered from low resolution, defocussing and other artefacts.

VideoSAR is a land-imaging mode where the synthetic aperture radar (SAR) is operated in a spotlight configuration for an extended period of time. A sequence of images is continuously formed to a common Cartesian grid, while the radar is either flying towards, by or circling a target area. In general, VideoSAR imaging maintains antenna illumination on a target regardless of changes in squint angle, within theoretical and practical limitations. The video-like nature is a result of the imagery being produced using overlapped synthetic apertures such that the output frame rate can be commensurate with that of a video system. Enhanced exploitation of this product typically requires that the imagery be phase preserving, and that the data acquisition geometries be highly controlled for generation of secondary products such as coherence images (coherence maps) for change detection. One of the most accurate image formation algorithms for the formation of phase preserved imagery is the back-projection algorithm. Though computationally intensive, its attraction lies in its simplicity, the fact that the relative location of each pixel in the output imagery is precisely known and the complex imagery is phase preserved. The backprojection algorithm is presented in terms of its application to VideoSAR imaging. Mathematical decomposition techniques for improving the computational efficiency of the algorithm are reviewed. The application of change detection between pairs of VideoSAR images and `stacks' of VideoSAR images where the benefits of trading off spatial image filtering and temporal image filtering by averaging coherence in slow-time is also examined. Finally, computer topologies applicable to back projection are outlined from the perspectives of both the signal processing architecture and advances in massively parallel computing.

This chapter presents the principle of high-resolution wide-swath synthetic aperture radar (SAR), a means for imaging wide areas at high resolution. The material covers the limitations of achieving wide-swath and high-resolution with a traditional SAR, the basic idea of using a multi-aperture SAR to overcome this limitation and current implementations where multi-aperture (or multiple antenna) systems collect data in an ideal configuration. Overviews of approaches to processing data collected in nonideal configurations, such as when the data are collected with non-uniform sampling and/or when they are collected with a squinted system, are then introduced. Armed with an overview, the chapter introduces the theory of multi-aperture SAR processing with the objective of generalizing the concept of high-resolution wide-swath to higher resolution, wider-swath SAR. This enables application of the added degrees of freedom to other modes such as spotlight and high-resolution stripmap. In order to present the theory and the generalizations, and in consideration of possible future systems, the theory is derived in the wavenumber domain for wideband and/or widebeam, space-based systems with special cases for narrowband systems presented as appropriate. In contrast to much of the current literature, the theory views the antenna patterns as the key provider of the additional degrees of freedom and proposes to utilize other pattern characteristics in addition to the phase-centre separation to improve imaging. For this reason, special care is taken in developing the antenna pattern dependence in the signal model. The approach for signal reconstruction focuses, mainly, on the minimum mean-square error method as it is quite general and includes, as special cases, the well-known projection approach as well as the space-time adaptive processing (STAP) approach. Further, it inherently, simultaneously improves the geometrical and radiometrical resolution due to favourable weighting by the antenna pattern and a less aggressive ambiguity prescription as compared to other techniques. The approach also naturally incorporates other more generalized system configurations where, for instance, the antenna patterns have, not only different phase-centres, but also different shapes or different pointing directions. As an added feature, the presented method is robust against matrix inversion problems which can render the projection approach intractable. The special case of a phased-array multi-aperture system is presented.

SAR interferometry has been developed to estimate the precise elevation of surfaces that reflect radar signals, thus generating digital elevation models, which can be accurate to the metre. Further, it is possible to monitor slow motion of temporally stable targets with millimetre accuracy. Topics as interferometric data processing, performance evaluation, singleand multi-baseline acquisitions, mechanical stability of the targets (coherence) and differential InSAR stacks processing (persistent scatterers, SBAS) are introduced and discussed. The major applications for land, solid Earth and infrastructure monitoring are presented. If the radar faces targets like forests and glaciers, where electromagnetic wave penetration allows backscatter at different depths, it is then possible to separate the layers using SAR tomography, also discussed. Finally, an overview is made of the most challenging future interferometric systems, like passive bistatic companions or geosynchronous SAR.

Today's demand for space-borne Synthetic Aperture Radar (SAR) data has grown to the point where significant commercial funding of advanced space-borne radar system development has been being made available. The current generation of commercial space-based SAR imaging satellites, such as RADARSAT-2, Sentinel-1, TerraSAR-X/TanDEM-X (PAZ), COSMO-SkyMED and ALOS-2, operate at a single frequency (L-, Cand X-band) and are based on active phased array antenna technology that offers beam agility and adds polarization diversity. Consequently, these modern satellites are equipped with more than one receive channel (i.e., AD-converter) that can also be utilized to record measurements from multiple apertures in along-track direction. This is the principal prerequisite for a ground moving target indication (GMTI)1 capability. While space-based SAR GMTI offers many advantages like global ground coverage and access to strategic regions, it also faces several obstacles such as high satellite velocity, Earth rotation and oftentimes small target reflection energy caused by the enormous distances of more than 1,000 km among others. This book chapter presents the state-of-the-art of space-based SAR-GMTI science and technology with focus on recent advances and the latest direction of research and development (R&D) activities. Owing to an exponential cost jump, technological advances of space-based radars especially with regard to increased power, increased aperture sizes and additional receiver channels have only been somewhat incremental in the last decades. Spacecraft with more than two parallel receive paths are only expected to materialize two generations down the line. Hence, current R&D put emphasis on innovative new concepts trying to circumvent these technological limitations thereby often pushing the resources on existing SAR payloads to their limits.2 Virtually all of these concepts are accompanied by cutting-edge but complex and resource-hungry signal-processing algorithms that only recently became feasible based on the fast-paced evolution in computing power over the last decade. Many of the presented proof-of-concept studies are considered building blocks of future operational space-based SAR capabilities, for instance, the synergy between high-resolution-wide-swath (HRWS) imaging and motion indication and estimation. This chapter attempts to provide a comprehensive, in-depth overview of the theory and the radar signalprocessing techniques required for space-based SAR-GMTI corroborated by real multichannel data from RADARSAT-2.

Multipass differential synthetic aperture radar (SAR) interferometry (DInSAR), with its capability to accurately monitor ground displacements, has dramatically pushed the applications of imaging radars in many fields, particularly in the area of environmental risk monitoring and security. Advanced DInSAR techniques operating at reduced resolution allow monitoring very wide areas whereas persistent scatterers interferometry (PSI) and more recently SAR tomography for very high resolution sensors, have shown to be powerful methods for providing 3-D point clouds representing buildings and infrastructures as well as in the monitoring of their possible slow temporal deformations. In this chapter, a detailed description of these techniques is provided, starting on the basic principles of the SAR interferometry and highlighting the relationship between interferometric and tomographic approaches.

This chapter presents a novel simultaneous monostatic and bistatic ground moving target indication (GMTI) mode for improved target detection and imaging capability. The mode uses an airborne multichannel radar system and a stationary transmitter. Both systems transmit simultaneously on adjacent frequency bands, and the airborne multichannel system receives both its monostatic echoes and the bistatic returns. Geometrical diversity between the monostatic and the bistatic measurements enhances moving target-detection capabilities. Moreover, for movers detected in both datasets, an estimation of the target velocity vector (i.e., velocity and direction of motion) can be performed. By simply extracting a singlechannel dataset, this also allows correct focusing of moving targets both in monostatic and in bistatic datasets, if SAR-GMTI capability is required. Consequently, situational awareness over the observed area is greatly improved. The effectiveness of the proposed technique is analyzed both from a theoretical point of view and by means of an ad-hoc experiment conducted by the Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR) in fall 2013.

The exploitation of ISAR data simultaneously acquired by multiple radar systems is considered in this chapter in order to enhance the quality of ISAR images of moving targets with respect to the conventional single-sensor case, thus making ISAR images more effective when used for target classification and recognition. In particular, multi-sensor data are exploited in order to increase the cross-range resolution of ISAR images of rotating targets and to improve the accuracy in the estimation of the target motion. The distributed (multi-sensor) ISAR technique is devised for two different cases: (i) MIMO case with each platform carrying an active radar, that transmits and receives RF waveforms, (ii) multistatic case with a single platform carrying an active radar (transmitting and receiving) and the remaining platforms equipped with passive sensors (namely receiving only). For such distributed imaging system: (a) the PSF is derived showing the capability at providing an increase of the crossrange resolution up to the number of platforms in the multistatic case and even higher in the MIMO case; (b) the required focusing technique is also presented and discussed following a decentralized approach; (c) multi-sensor based target motion estimation techniques are considered showing the performance improvement with respect to the conventional single-sensor case. This distributed ISAR system could be of great benefit in applications where the target rotation angle is insufficient to guarantee the desired resolution. A typical case is the imaging of ship targets with rotation induced by the sea swell structure under low sea state conditions. Results obtained against synthetic ISAR data are presented; moreover, experimental data collected in an anechoic chamber against different targets on a rotating platform are processed by following the presented distributed ISAR technique to validate the approach.

Imaging of moving scatterers in a synthetic aperture radar (SAR) image is a challenging problem as targets will both smear and be displaced in azimuth. One technique which overcomes this limitation is the velocity SAR (VSAR) algorithm which was originally proposed by Friedlander and Porat in 1997. This technique provides the full Doppler spectrum at each and every pixel of a SAR image, which then allows for correction of the distortion caused by the radial motion. This chapter describes the signal processing of the VSAR algorithm and presents the first experimental results using both land and airborne multi-channel radar systems developed by the US Naval Research Laboratory (NRL). These examples demonstrate the VSAR correction for a variety of moving backscatter sources, including automobiles, ships, shoaling ocean waves and tidal currents. Two further applications of VSAR are also briefly covered in the chapter. These being the application to target detection and velocity inverse SAR.

Passive and multistatic radar

Due to the recently renewed interest inside the radar community, bistatic and multistatic radar are again a hot topic of research, despite the fact that their history is almost as old as the history of radar itself. This history is also quite well documented and revised. Therefore, revising this whole history is outside of the scope of this introduction. However, a few historic elements are recalled below to show how the contributions presented in this chapter find an especially important place among the novel radar techniques and applications.

This chapter addresses the study of the properties of bistatic clutter, compared with monostatic with a particular focus on recent results from analysis of real sea clutter data. Clutter is usually defined as the unwanted radar returns from land, sea, rainfall or other phenomena, which may mask the echoes from targets, and its models usually reproduce the normalized radar cross section, spatial and temporal correlation properties, statistical variability and Doppler spectrum. Such models are important in order to develop target detection techniques and evaluate their performance under variable environmental conditions. The properties of monostatic clutter are influenced by radar parameters - i.e. frequency, resolution cell size, incidence angle and polarization - and environmental conditions. In the case of sea clutter, important environmental parameters are: wind and wave direction, level of development of the sea and depth of the sea. On the other hand, bistatic clutter is heavily influenced by the system geometry, i.e. the relative position of the radar receivers with respect to the transmitters, resulting in new degrees of freedom to include in future models.

Forward scatter radar (FSR) is historically thought of as the first type of bistatic radar. Research on FSR has been predominantly focussed on its ability to serve as an electronic fence, and it has already proven the FSR's excellent detection and target motion parameter estimation capabilities. Recently, a wave of interest has emerged in FSR; first, this is a consequence of the introduction of `stealth' targets. These targets have a significantly reduced radar cross-section (RCS) because of their specific shapes and/or coatings which may greatly suppress backscattering, yet their shadows will still render them perfectly `visible' to FSR. Second, interest in FSR has appeared because of the establishment of passive coherent location concepts where illuminators of opportunity are used to form a bistatic radar network. This chapter provides an overview of FSR, theory and phenomenology and further discusses its capabilities and limitations.

Through-the-wall radar imaging (TWRI) provides the capability to see inside buildings using electromagnetic waves for both defence and civilian applications. Unlike traditional free-space radar operation, the presence of walls introduces unique challenges that need to be addressed to render TWRI a viable technology. In this chapter, we present a review of various approaches for building layout determination and imaging of stationary indoor scenes. These approaches effectively address the associated challenges to provide effective and reliable radar operation. Specifically, we consider both ground-based and airborne radar operation and discuss both non-adaptive time-domain and frequency-domain beamforming approaches for through-the-wall image formation of stationary scenes and feature extraction-based building layout determination methods.

The potentialities of passive radar (PR) will be illustrated in this chapter with reference to the surveillance of areas of limited extent. Despite the exploitation of the PR principle in short-range applications could appear as a simple task, it is shown that it brings a number of challenging issues that must be solved to benefit from its potential advantages. In addition, the requirements on such sensors could be much more demanding to enable advanced capabilities. The chapter attempts to give the reader an insight into the real-world applications of short-range PR. The considered applications span from maritime traffic control, to vehicular traffic monitoring, and indoor surveillance. For each case, experimental results are reported, obtained in different scenarios when exploiting different waveforms of opportunity. Walking through these results also gives the chance to describe some technical aspects related to system design issues and signal processing techniques.

This chapter is an introduction to passive synthetic aperture radar (SAR) using global navigation satellite systems (GNSS), as illuminators of opportunity. Such systems include the global positioning system (GPS), the Russian GLONASS system, or the forthcoming Galileo constellation. Apart from the traditional benefits of a passive radar system, which include cost efficiency, license-free and covert operation, GNSS-based systems have a number of relative merits compared to passive systems based on terrestrial illuminating sources. One of its most prominent features is the potential for persistent monitoring anywhere in the world due to the global GNSS coverage. In addition, as GNSS guarantee a number of satellites illuminating the same point on Earth from multiple aspect angles simultaneously, this provides an opportunity to enhance radar information space. This can be done either by comparing individual passive SAR images obtained from multiple satellite perspectives, or combining them using multistatic SAR techniques. On the other hand, the relatively low power flux density near the Earth's surface restricts the field of view of applications for such a system to monitoring local areas. At the same time, and partially due to its rather broad scope, this technology has not yet reached the maturity of terrestrial-based systems. As such, the purpose of this chapter is to present methods and results on the fundamental science and technology behind GNSS-based SAR, as a stepping stone to realizing its full potential. This includes an introduction to the system concept and its fundamental parameters (power budget/resolution), signal processing algorithms for signal synchronization and image formation, as well as proof-of-concept results for advanced techniques such as change detection, multi-perspective and multistatic imaging.

Passive radar is one of the most rapidly developing fields in the radar technology in recent years. The ground-based passive radar technology is now entering a stage of maturity. In the past, in a case of active radars, the technology developed for ground-based sensing was adapted for airborne platforms. The same trend has been observed in the passive radar technology as it is being adapted for moving platforms, mostly the airborne ones. Major aims of this adaptation include, among others, protection of the platforms, detection of airborne and surface moving targets and remote sensing, including SAR and ISAR imaging. The use of mobile PCL systems provides an extended functionality in comparison to the one of the stationary ground-based radars. The chapter consists of two key sections. The first section presents the passive imaging technique based on a bistatic SAR concept, the second section discusses the main challenges for the airborne passive radars, in particular a cancelation of Doppler spread clutter, and also presents solutions based on the CLEAN techniques, DPCA and space-time adaptive processing. All theoretical considerations are illustrated with the simulation and experimental validation examples.

Passive radar (PR) is always dependent on illuminators of opportunity and the waveform they provide. Using multiple different illumination sources as well as the distribution of multiple sensors in multistatic networks can provide some diversity. As examples some multistatic configurations and multi-band passive coherent location (PCL)-systems are sketched indicating the wide range of applications. Furthermore, the properties of illuminators, which can be used for medium range PR processing, like frequency modulated radio, digital audio broadcast and digital video broadcast terrestrial are described. Special signal processing approaches are highlighted and a hybrid PR system and processing concept is explained. The chapter is completed with some measurement results from multistatic experiments and a civilian PCL-network application of a multistatic PCL system.

In distributed sensing applications, the nodes of a sensor network cooperatively detect and localize targets of interest. In particular, active multiple-input multipleoutput (MIMO) radar (AMR) uses multiple transceivers to transmit separable signals and receive the scattered returns, while passive MIMO radar (PMR) uses multiple receivers to receive the direct-path (transmitter-to-receiver) and target-path (transmitter-to-target-to-receiver) signals originated by multiple non-cooperative transmitters to detect and localize targets. This chapter surveys recent results in the theory of centralized detection in PMR networks. Generalized likelihood ratio test (GLRT)-based detectors for PMR detection have been developed and their performances are compared to related detectors for AMR and passive source localization (PSL) sensor networks. PMR detection sensitivity and ambiguity are then analyzed as a function of both the target-path and direct-path signals-to-noise ratios (SNRs). The results demonstrate that PMR detection sensitivity and ambiguity approach that of active MIMO radar sensor networks when the direct-path SNRs are sufficiently high. Conversely, PMR detection sensitivity and ambiguity approximate that of passive source localization sensor networks when the direct-path SNRs are sufficiently low. In this way, PMR networks unify these related active and passive sensor network architectures in a common theoretical framework.